Book description
Unlock Java's full potential for cloud computing through expert insights from real-world case studies and stay ahead with the latest trends in agile and robust Java application development
Key Features
- Master concurrency and parallelism to overcome cloud computing challenges in Java
- Build scalable solutions with Big Data, ML, microservices, and serverless architectures
- Explore cloud scaling, GPU utilization, and future tech innovations in Java applications
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
If you’re a software developer, architect, or systems engineer, exploring Java’s concurrency utilities and synchronization in the cloud, this book is an essential resource. Tech visionary Jay Wang, with over three decades of experience transforming industry giants, brings unparalleled expertise to guide you through Java’s concurrency and parallel processing in cloud computing.
This comprehensive book starts by establishing the foundational concepts of concurrency and parallelism, vital for cloud-native development, and gives you a complete overview, highlighting challenges and best practices. Wang expertly demonstrates Java’s role in big data, machine learning, microservices, and serverless computing, shedding light on how Java’s tools are effectively utilized in these domains. Complete with practical examples and insights, this book bridges theory with real-world applications, ensuring a holistic understanding of Java in cloud-based scenarios. You’ll navigate advanced topics, such as synchronizing Java’s concurrency with cloud auto-scaling and GPU computing, and be equipped with the skills and foresight to tackle upcoming trends in cloud technology.
This book serves as your roadmap to innovation and excellence in Java cloud applications, giving you in-depth knowledge and hands-on practice for mastering Java in the cloud era.
What you will learn
- Understand Java concurrency in cloud app development
- Get to grips with the core concepts of serverless computing in Java
- Boost cloud scaling and performance using Java skills
- Implement Java GPU acceleration for advanced computing tasks
- Gain insights into Java's role in the evolving cloud and AI technology
- Access hands-on exercises for real-world Java applications
- Explore diverse Java case studies in tech and fintech
- Implement Java in AI-driven cloud and data workflows
- Analyze Java's application in IoT and real-time analytics
Who this book is for
This book is for Java developers, software engineers, and cloud architects with intermediate Java knowledge. It's ideal for professionals transitioning to cloud-native development or seeking to enhance their concurrent programming skills. DevOps engineers and tech leads involved in cloud migration will also find valuable insights. Basic Java proficiency, familiarity with cloud concepts, and some experience with distributed systems is expected.
Table of contents
- Java Concurrency and Parallelism
- Contributors
- About the author
- About the reviewers
- Preface
- Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing
-
Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape
- Technical requirements
- The dual pillars of concurrency versus parallelism – a kitchen analogy
- Java and the cloud – a perfect alliance for cloud-native development
-
Modern challenges in cloud-native concurrency and Java’s weapons of choice
- Wrangling distributed transactions in Java – beyond classic commits
- Maintaining data consistency in cloud-native Java applications
- Handling state in microservices architectures
- Cloud database concurrency – Java’s dance moves for shared resources
- Parallelism in big data processing frameworks
- Cutting-edge tools for conquering cloud-native concurrency challenges
- Conquering concurrency – best practices for robust cloud-native applications
- Summary
- Exercise – exploring Java executors
- Questions
-
Chapter 2: Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond
- Technical requirements
- Java’s kitchen of concurrency – unveiling threads and processes
- The concurrency toolkit – java.util.concurrent
- Synchronization and locking mechanisms
- Employing Future and Callable for result-bearing task execution
- Safe data sharing between concurrent tasks
- Leveraging thread-safe collections to mitigate concurrency issues
- Concurrent best practices for robust applications
- Summary
- Questions
- Chapter 3: Mastering Parallelism in Java
-
Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era
- Technical requirements
- Introduction to Java concurrency tools – empowering cloud computing
- Taming the threads – conquering the cloud with the Executor framework
- Real-world examples of thread pooling and task scheduling in cloud architectures
- Utilizing Java’s concurrent collections in distributed systems and microservices architectures
- Advanced locking strategies for tackling cloud concurrency
- Advanced concurrency management for cloud workflows
- Utilizing tools for diagnosing concurrency problems
- The quest for clarity – advanced profiling techniques
- Weaving the web – integrating profiling tools into CI/CD pipelines
- Service mesh and APM – your cloud performance powerhouse
- Mastering concurrency in cloud-based Java applications – testing and debugging tips
- Summary
- Questions
-
Chapter 5: Mastering Concurrency Patterns in Cloud Computing
- Technical requirements
- Core patterns for robust cloud foundations
- Java concurrency patterns for asynchronous operations and distributed communications
- Combining concurrency patterns for enhanced resilience and performance
- Blending concurrency patterns – a recipe for high-performance cloud applications
- Summary
- Questions
- Part 2: Java's Concurrency in Specialized Domains
-
Chapter 6: Java and Big Data – a Collaborative Odyssey
- Technical requirements
- The big data landscape – the evolution and need for concurrent processing
- Hadoop – the foundation for distributed data processing
- Java and Hadoop – a perfect match
- Beyond the basics – advanced Hadoop concepts for Java developers and architects
- Hadoop and Spark equivalents in major cloud platforms
- Real-world Java and big data in action
- Summary
- Questions
- Chapter 7: Concurrency in Java for Machine Learning
-
Chapter 8: Microservices in the Cloud and Java’s Concurrency
- Technical requirements
- Core principles of microservices – architectural benefits in cloud platforms
- Essential Java concurrency tools for microservice management
-
Challenges and solutions in microservices concurrency
- Bottlenecks – diagnosing potential challenges in concurrent microservices architectures
- Consistency – ensuring data consistency and smooth inter-service communication
- Resilience – achieving system resilience and fault tolerance
- Practical design and implementation – building effective Java microservices
- Strategic best practices – deploying and scaling microservices
- Advanced concurrency patterns – enhancing microservice resilience and performance
- Summary
- Questions
-
Chapter 9: Serverless Computing and Java’s Concurrent Capabilities
- Technical requirements
- Fundamentals of serverless computing in java
- Adapting Java’s concurrency model to serverless environments
- Introducing serverless frameworks and services – AWS SAM, Azure Functions Core Tools, Google Cloud Functions, and Oracle Functions
- Industry examples – Java serverless functions with a focus on concurrency
- Building with serverless frameworks – a practical approach
- Summary
- Questions
- Part 3: Mastering Concurrency in the Cloud – The Final Frontier
-
Chapter 10: Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics
- Technical requirements
- Fundamentals of cloud auto-scaling – mechanisms and motivations
-
Java’s concurrency models – alignment with scaling strategies
- Optimizing Java applications for cloud scalability – best practices
- Code example – best practices in optimizing a Java application for auto-scaling with AWS services and Docker
- Monitoring tools and techniques for Java applications
- Real-world case studies and examples
- Practical application – building scalable Java-based solutions for real-time analytics and event-driven auto-scaling
- Advanced topics
- Summary
- Questions
- Chapter 11: Advanced Java Concurrency Practices in Cloud Computing
- Chapter 12: The Horizon Ahead
- Appendix A: Setting up a Cloud-Native Java Environment
-
Appendix B: Resources and Further Reading
- Recommended books, articles, and online courses
- Chapters 10–12
-
Answers to the end-of-chapter multiple-choice questions
- Chapter 1: Concurrency, Parallelism, and the Cloud: Navigating the Cloud-Native Landscape
- Chapter 2: Introduction to Java’s Concurrency Foundations: Threads, Processes, and Beyond
- Chapter 3: Mastering Parallelism in Java
- Chapter 4: Java Concurrency Utilities and Testing in the Cloud Era
- Chapter 5: Mastering Concurrency Patterns in Cloud Computing
- Chapter 6: Java and Big Data – a Collaborative Odyssey
- Chapter 7: Concurrency in Java for Machine Learning
- Chapter 8: Microservices in the Cloud and Java’s Concurrency
- Chapter 9: Serverless Computing and Java’s Concurrent Capabilities
- Chapter 10: Synchronizing Java’s Concurrency with Cloud Auto-Scaling Dynamics
- Chapter 11: Advanced Java Concurrency Practices in Cloud Computing
- Chapter 12: The Horizon Ahead
- Index
- Other Books You May Enjoy
Product information
- Title: Java Concurrency and Parallelism
- Author(s):
- Release date: August 2024
- Publisher(s): Packt Publishing
- ISBN: 9781805129264
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